User interactions with personal assistants like Alexa, Google Home and Siri are typically initiated by a wake term or wakeword. Several personal assistants feature "follow-up" modes that allow users to make additional interactions without the need of a wakeword. For the system to only respond when appropriate, and to ignore speech not intended for it, utterances must be classified as device-directed or non-device-directed. State of the art systems have largely used acoustic features for this task, while others have used only lexical features or have added LM-based lexical features. We propose a directedness classifier that combines semantic lexical features with a lightweight acoustic feature and show it is effective in classifying directedness. The mixed-domain lexical and acoustic feature model is able to achieve 14% relative reduction of EER over a state of the art acoustic-only baseline model. Finally, we successfully apply transfer learning and semi-supervised learning to the model to improve accuracy even further.

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Latent factor models play a dominant role among recommendation techniques. However, most of the existing latent factor models assume embedding dimensions are independent of each other, and thus regrettably ignore the interaction information across different embedding dimensions. In this paper, we propose a novel latent factor model called COMET (COnvolutional diMEnsion inTeraction), which provides the first attempt to model higher-order interaction signals among all latent dimensions in an explicit manner. To be specific, COMET stacks the embeddings of historical interactions horizontally, which results in two "embedding maps" that encode the original dimension information. In this way, users' and items' internal interactions can be exploited by convolutional neural networks with kernels of different sizes and a fully-connected multi-layer perceptron. Furthermore, the representations of users and items are enriched by the learnt interaction vectors, which can further be used to produce the final prediction. Extensive experiments and ablation studies on various public implicit feedback datasets clearly demonstrate the effectiveness and the rationality of our proposed method.

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